A practical framework to assess the hydrodynamic impact of ship waves on river banks
Bibliographic record
Abstract
Abstract The temporal alteration of the hydrodynamic regime in rivers due to navigation has significant effects on riverine ecosystems. Most of the key mechanisms and interactions between hydrodynamic and ecological variables have already been revealed; however, the quantitative evaluation of biotic and abiotic variables still stands a challenge. This paper aims to present a thorough, spatiotemporal framework, involving field and computational tools, for the assessment of wave hydrodynamics in the littoral zone of rivers, where its ecological relevance is the most significant. The temporal variation of significant wave heights is derived from high‐frequency pressure measurements, offering a well‐comparable statistical evaluation of individual wave events considering the duration of different wave intensities. Acoustic Doppler velocimetry (ADV) is used for the assessment of near‐bank velocities, from which the secondary wave‐related components have been filtered offering relevant validation data for numerical modelling. The small footprint of the ADVs is extended with up‐to‐date computational fluid dynamics modelling. Wave spectra derived from the pressure measurements are used as boundary conditions for phase‐resolved irregular wave modelling with the level set method based numerical model REEF3D. The properly validated model offers the assessment of the most relevant hydrodynamic variables (e.g., velocity or turbulent kinetic energy) in a spatially extended manner, from which the hydrodynamic footprint of the wave events can be interpreted in a statistical fashion. The implementation of the proposed framework is illustrated through a case study at a Hungarian section of the Danube River.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".